虚拟现实辅助机器人遥操作技术研究

本文以水下机器人的遥操作作业为应用背景,提出并实现了虚拟现实技术和视觉感知 信息辅助机器人遥操作实验系统．该系统使用了CAD模型和立体视觉信息完成遥操作机器人 及其作业环境的几何建模和运动学建模,实现了虚拟作业环境的生成和实时动态图形显示． 采用了基于立体视觉的虚拟环境与真实环境的一致性校正、图形图像叠加、作业体与环境位 姿关系建立、基于网络的监控通讯等关键技术．在这个实验系统中,操作人员可利用所生成 的虚拟环境,在多视点、多窗口作业状态图形和图像显示帮助下,实时动态地进行作业观测 与机器人遥操作与运动规划,为先进遥操作机器人系统的实现提供了经验和关键技术．     

Space-variant motion detection for active visual target tracking

A biologically inspired approach to active visual target tracking is presented. The approach makes use of three strategies found in biological systems: space-variant sensing, a spatio-temporal-frequency-based model of motion detection and the alignment of sensory-motor maps. Space-variant imaging is used to create a 1D array of elementary motion detectors (EMDs) that are tuned in such a way as to make it possible to detect motion over a wide range of velocities while still being able to detect motion precisely. The array is incorporated into an active visual tracking system. A method of analysis and design for such a tracking system is proposed. It makes use of a sensory-motor map which consists of a phase-plane plot of the continuous-time dynamics of the tracking system overlaid onto a map of the detection capabilities of the array of EMDs. This sensory-motor map is used to design a simple 1D tracking system and several simulations show how the method can be used to control tracking performance using such metrics as overshoot and settling time. A complete 1D active vision system is implemented and a set of simple target tracking experiments are performed to demonstrate the effectiveness of the approach.     

Active vision and receptive field development in evolutionary robots

In this paper, we describe the artificial evolution of adaptive neural controllers for an outdoor mobile robot equipped with a mobile camera. The robot can dynamically select the gazing direction by moving the body and/or the camera. The neural control system, which maps visual information to motor commands, is evolved online by means of a genetic algorithm, but the synaptic connections (receptive fields) from visual photoreceptors to internal neurons can also be modified by Hebbian plasticity while the robot moves in the environment. We show that robots evolved in physics-based simulations with Hebbian visual plasticity display more robust adaptive behavior when transferred to real outdoor environments as compared to robots evolved without visual plasticity. We also show that the formation of visual receptive fields is significantly and consistently affected by active vision as compared to the formation of receptive fields with grid sample images in the environment of the robot. Finally, we show that the interplay between active vision and receptive field formation amounts to the selection and exploitation of a small and constant subset of visual features available to the robot.     

Attentive behavior in an anthropomorphic robot vision system

Powerful data reduction and selection processes, such as selective attention mechanisms and space-variant sensing in humans, can provide great advantages for developing effective real-time robot vision systems. The use of such processes should be closely coupled with motor capabilities, in order to actively interact with the environment. In this paper, an anthropomorphic vision system architecture integrating retina-like sensing, hierarchical structures and selective attention mechanisms is proposed. Direction of gaze is shifted based on both the sensory and semantic characteristics of the visual input, so that a task-dependent attentive behavior is produced. The sensory features currently included in the system are related to optical flow invariants, thus providing the system with motion detection capabilities. A neural network architecture for visual recognition is also included, which produces semantic-driven gaze shifts.     

一种基于机器人的航天器大型部件自主装配方法

航天器大型部件在狭小空间中装配存在着视野受限、目标位置不可见等问题,传统的吊装方法无法精确调整位姿、磕碰风险极高．本文提出一种基于双目视觉定位的机器人辅助装配路径规划方法．该方法以航天器、待安装部件以及机器人等装配要素的3维模型为基础,采用双目视觉系统对航天器上安装位置的几何特征进行精确定位．利用测量结果确定机器人和航天器之间的相对位姿关系,进而构建航天器装配现场的虚拟环境,识别装配过程中的几何约束,然后利用轴对齐包围盒方法进行碰撞检测,采用随机路图法规划出一条无干涉的装配路径,最后利用离线编程技术生成机器人可执行的装配序列．以某型号航天器激光测高仪为对象开展装配试验,完成了大型部件在狭小凹舱内的无磕碰安装．结果表明该方法可以实现对螺纹孔精准定位,快速规划出无干涉装配路径,能够控制机器人安全、高效完成大型部件在狭小空间下的安装作业．     

Precise Positioning of Binocular Eye-to-Hand Robotic Manipulators

This article addresses the visual servoing of a rigid robotic manipulator equipped with a binocular vision system in eye-to-hand configuration. The control goal is to move the robot end-effector to a visually determined target position precisely without knowing the precise camera model. Many vision-based robotic positioning systems have been successfully implemented and validated by supporting experimental results. Nevertheless, this research aims at providing stability analysis for a class of robotic set-point control systems employing image-based feedback laws. Specifically, by exploring epipolar geometry of the binocular vision system, a binocular visual constraint is found to assist in establishing stability property of the feedback system. Any three-degree-of-freedom positioning task, if satisfying appropriate conditions with the image-based encoding approach, can be encoded in such a way that the encoded error, when driven to zero, implies that the original task has been accomplished with precision. The corresponding image-based control law is proposed to drive the encoded error to zero. The overall closed-loop system is exponentially stable provided that the binocular model imprecision is small.     

Embedded visual behaviors for navigation

The implementation of a set of visually based behaviors for navigation is presented. The approach, which has been inspired by insect's behaviors, is aimed at building a “library” of embedded visually guided behaviors coping with the most common situations encountered during navigation in an indoor environment. Following this approach, the main goal is no longer how to characterize the environment, but how to embed in each behavior the perceptual processes necessary to understand the aspects of the environment required to generate a purposeful motor output.

The approach relies on the purposive definition of the task to be solved by each of the behaviors and it is based on the possibility of computing visual information during the action. All the implemented behaviors share the same input process (partial information of the image flow field) and the same control variables (heading direction and velocity) to demonstrate both the generality of the approach as well as its efficient use of the computational resources. The controlled mobile base is supposed to move on a flat surface but virtually no calibration is required of the intrinsic and extrinsic parameters of the two cameras and no attempt is made at building a 2D or 3D map of the environment: the only output of the perceptual processes is a motor command.

The first behavior, the centering reflex allows a robot to be easily controlled to navigate along corridors or following walls of a given scene structure. The second behavior extends the system capabilities to the detection of obstacles lying on the pavement in front of the mobile robot. Finally docking behaviors to control the robot to a given position in the environment, with controlled speed and orientation, are presented.

Besides the long-term goal of building a completely autonomous system, these behaviors can have very short-term applications in the area of semi-autonomous systems by taking care of the continuous, tedious control required during routine navigation.     

Detection and tracking for robotic visual servoing systems

Robot manipulators require knowledge about their environment in order to perform their desired actions. In several robotic tasks, vision sensors play a critical role by providing the necessary quantity and quality of information regarding the robot's environment. For example, “visual servoing” algorithms may control a robot manipulator in order to track moving objects that are being imaged by a camera. Current visual servoing systems often lack the ability to detect automatically objects that appear within the camera's field of view. In this research, we present a robust “figureiground” framework for visually detecting objects of interest. An important contribution of this research is a collection of optimization schemes that allow the detection framework to operate within the real-time limits of visual servoing systems. The most significant of these schemes involves the use of “spontaneous” and “continuous” domains. The number and location of continuous domains are. allowed to change over time, adjusting to the dynamic conditions of the detection process. We have developed actual servoing systems in order to test the framework's feasibility and to demonstrate its usefulness for visually controlling a robot manipulator.     

Modeling selective attention using a neuromorphic analog VLSI device

Attentional mechanisms are required to overcome the problem of flooding a limited processing capacity system with information. They are present in biological sensory systems and can be a useful engineering tool for artificial visual systems. In this article we present a hardware model of a selective attention mechanism implemented on a very large-scale integration (VLSI) chip, using analog neuromorphic circuits. The chip exploits a spike-based representation to receive, process, and transmit signals. It can be used as a transceiver module for building multichip neuromorphic vision systems. We describe the circuits that carry out the main processing stages of the selective attention mechanism and provide experimental data for each circuit. We demonstrate the expected behavior of the model at the system level by stimulating the chip with both artificially generated control signals and signals obtained from a saliency map, computed from an image containing several salient features.     

基于多人协同虚拟现实的主变应急培训应用研究

为了提高主变应急培训能力,提出基于多人协同虚拟现实的主变应急培训视景仿真模型.以火灾应急演练为研究对象,构建主变应急培训的虚拟视景图像重构模型,并对虚拟视景环境参数进行结算,根据参数结果的计算,进行主变应急培训过程中的虚拟现实仿真,实现主变应急培训的虚拟现实VR仿真模型的优化设计.测试结果表明,采用该方法进行主变应急培训的虚拟视景仿真的协同性较好,图像融合性能较高,视景重构能力较强.     

Sound and Visual Tracking for Humanoid Robot

Mobile robots capable of auditory perception usually adopt the stop-perceive-act principle to avoid sounds made during moving due to motor noise. Although this principle reduces the complexity of the problems involved in auditory processing for mobile robots, it restricts their capabilities of auditory processing. In this paper, sound and visual tracking are investigated to compensate each other's drawbacks in tracking objects and to attain robust object tracking. Visual tracking may be difficult in case of occlusion, while sound tracking may be ambiguous in localization due to the nature of auditory processing. For this purpose, we present an active audition system for humanoid robot. The audition system of the highly intelligent humanoid requires localization of sound sources and identification of meanings of the sound in the auditory scene. The active audition reported in this paper focuses on improved sound source tracking by integrating audition, vision, and motor control. Given the multiple sound sources in the auditory scene, SIG the humanoid actively moves its head to improve localization by aligning microphones orthogonal to the sound source and by capturing the possible sound sources by vision. The system adaptively cancels motor noises using motor control signals. The experimental result demonstrates the effectiveness of sound and visual tracking.     

Active vision for sociable robots

Ballard (1991) described the implications of having a visual system that could actively position the camera coordinates in response to physical stimuli. In humanoid robotic systems, or in any animate vision system that interacts with people, social dynamics provide additional levels of constraint and additional opportunities for processing economy. In this paper, we describe an integrated visual-motor system that was implemented on a humanoid robot to negotiate the robot's physical constraints, the perceptual needs of the robot's behavioral and motivational systems, and the social implications of the motor acts     

Research on auxiliary methods of swimming training virtual simulation technology based on embedded computer✰

Virtual Reality (VR) is a partnership between an independent hardware and software integrated system that ships it into a nearly simulated environment. Studies have shown that technology modifies variables related to an individual's visual and motor skills. Therefore, there are many possible explanations for the positive effects of encounters in sports that need to be considered using virtual reality. Virtual Reality Optimization Technique (VROT) can also trigger motor learning in the brain responsible for motor activity, thus using motor imaging (MI) technology, even when no action is occurring. This application is used to train swimming. This provides the criteria for evaluation, and the evaluated group will receive the experimental group. Swimming is one of the most popular games to train students and perform better results using the virtual reality technique. Solving the swimming lesson system is improving the ecological validity of the study, maintaining the great potential for cognitive and behavioral research, and separating cognitive behavior. Perform a comparative analysis of the student's correct and incorrect swimming practice. The proposed system analyzes the result using virtual reality technique based on the FPGA controller and getting better performance results.     

平行视觉:基于ACP的智能视觉计算方法

在视觉计算研究中,对复杂环境的适应能力通常决定了算法能否实际应用,已经成为该领域的研究焦点之一.由人工社会（Artificial societies）、计算实验（Computational experiments）、平行执行（Parallel execution）构成的ACP理论在复杂系统建模与调控中发挥着重要作用.本文将ACP理论引入智能视觉计算领域,提出平行视觉的基本框架与关键技术.平行视觉利用人工场景来模拟和表示复杂挑战的实际场景,通过计算实验进行各种视觉模型的训练与评估,最后借助平行执行来在线优化视觉系统,实现对复杂环境的智能感知与理解.这一虚实互动的视觉计算方法结合了计算机图形学、虚拟现实、机器学习、知识自动化等技术,是视觉系统走向应用的有效途径和自然选择.     

双目视觉的新颖实时局部2维地图构建方法

根据轮式机器人移动的特点,提出一种采用双目视觉的新颖实时局部2维栅格地图构建算法。首先,提出虚拟高度线投影成像原理,将场景均匀栅格化,在栅格中引入虚拟高度线,并将其投影到立体视觉系统的立体图对中产生投影线,将求解场景点高度值的问题转化为在投影线上和水平视差搜索范围内寻找具有最大相似测度的对应点问题;然后,提出一种新颖的局部2维地图构建方法,该方法以机器人所能越过的最大高度为阈值,对高于阈值部分的虚拟高度投影线上的场景点由其在图像对中的相似测度确定其是否为障碍物区域。实验结果表明,该方法满足机器人导航所要求的有效性和实时性,并可应用到构建3维地图。     

虚拟驾驶员视觉感知模型研究

视觉是驾驶员获得驾驶信息的主要通道,虚拟驾驶员的视觉感知模型是驾驶行为建模与仿真的重要内容,直接影响驾驶行为仿真的逼真度。介绍了四种视觉感知建模方法,根据视觉感知系统的反馈性和选择性,建立了视觉感知模型。在模型中,将视觉感知分为感觉和知觉两个过程,并引入了注意力、记忆力、驾驶疲劳和驾驶经验等因素,对每个因素进行分析。通过对虚拟驾驶员视觉感知模型的仿真实验,验证了该模型的可靠性和有效性,为驾驶行为研究提供了基础。     

足球比赛仿真中虚拟人的感知建模研究

感知建模是虚拟人足球比赛仿真行为建模的一个重要部分,直接影响着虚拟人的行为决策.根据虚拟球员的注意力聚焦规律、视觉生理特点,提出了一种基于注意力聚焦机制的虚拟球员的智能化感知模型,包括虚拟视觉、虚拟听觉和感知聚焦器.在几何视觉模型基础上建立了虚拟视觉模型,采用包围盒技术设计了视野感知器,障碍物感知器采用人工势场法计算出虚拟球员的避碰情况下的视觉方向;根据足球比赛中球员的行为特点,设计了感知聚焦器.实验结果表明该模型是可行的.     

结合机器视觉的采摘机械手的定位仿真研究

对水果采摘机械手空间定位机理进行了研究,分析了双目立体视觉系统的定位误差并建立视觉误差补偿机制,利用虚拟机械手开发软件和CCD视觉硬件构建了仿真系统,通过双目立体视觉获取空间位置数据映射到虚拟环境下引导机械手进行模拟采摘。该系统利用多领域知识融合实现了采摘机构与视觉关联精确定位的仿真,能有效地指导实际作业环境中采摘机械手精确定位的优化设计。     

类人足球机器人决策系统的设计

类人机器人足球比赛是机器人足球比赛的最高赛事.类人足球机器人的决策系统是基于独立视觉的自主决策系统,很大程度上决定着比赛的胜败.介绍了自主研发的类人足球机器人决策系统的架构及实现方法,并在此基础上运用有限状态机理论,对单个机器人的自主进攻策略进行了详细分析和研究,真实环境中的实验及比赛结果证明了其有效性.该决策系统的设计及研究工作对基于自主决策的多智能体协作以及服务性机器人决策系统的研究都具有重要的价值.